Global Learning and Observations to Benefit the Environment (GLOBE)

A. Summary

1. The System

The GLOBE project uses environmental data to help students develop an understanding of the earth as an integrated ecological system.  From schools allover the world, GLOBE students provide important new data to help scientists.  GLOBE students also contribute their own analyses of data from their local sites.
Currently, there are four domains of GLOBE scientific research. Each is detailed in one of the GLOBE investigations (linked with the corresponding chapter of the GLOBETeacher's Guide). We briefly summarize:

In addition to these direct investigations, there are two supportive investigations included in GLOBE: For more information see Introduction.

2. Learning Goals

The GLOBE Program Overview names three project goals: Ideally, the students learn key science concepts along with science protocols. Through GLOBE activities, students deepen their understanding of global systems, explore data and issues of data quality, experience the scientific method, and design and implement their own investigations.
The Teacher's Guide is mostly concerned with how to collect data, and these parts are well designed and of high scientific and didactical quality.  However, the materials have relatively little to say about methods of analysis.  Thus, the main goal of GLOBE seems to be to have students learn to collect reliable data by learning to follow strict data-collection protocols to avoid errors.  This emphasis is understandable from the perspectiveof project developers because their main interest is in getting data from the students that is reliable and thus worth analyzing.  This goal should not be devaluated as such data could be an important component of effective global monitoring.

For more information see Introduction.

3. Available Data

The GLOBE data archive provides all data collected by participating schools.  The data appear quite reliable.  Before a school can submit data, it receives training in data collection. The measurement methods are described in the curriculum in great detail (see  Data Analysis in the Curriculum).
All available data sets can be downloaded as html- or text files, and these files can be opened by standard spread sheets and statistical analysis programs.
  • The data archive includes data of different topics, such as air temperature, cloud observation, precipitation rain, precipitation solid, surface water. All topics and variable names are listed in existingprotocols. The DataArchive  is very well structured. One can download the entire dataset or select subsets for downloading.  Data can be searched by date, protocol, location, and by school or city name.

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    One can also access the data used to produce the GLOBE maps. Thus, students can download  existing data for every single variable from all participating schools and also from more than 3000 weather stations world-wide.

    For more information see data and dataarchive .

    4. Supports for Data Analysis

    Software

    With the GLOBE data Visualization tool, students can see data displayed as either GLOBE Maps or GLOBE Graphs.  Additional display varieties include: The GLOBE Maps can display measurements of a variable on a particular day. Nearly all variables from existing Protocols can be displayed.
    All maps can be varied by means of the following options: For more information see tools for data analysis .

    Subject matter knowledge

    Students can get relevant subject-matter information in a number of ways.  First, the introductory chapter of each investigation in the Teacher'sGuide describes "big picture" and reviews important subject matter knowledge for that investigation. Second, the scientists'corner  includes the reports from the annual GLOBE conference, along with e-mail addresses of the involved scientists who students are encouraged to e-mail with questions.  Finally,  a resource room  has annotated links to relevant websites.

    Data analytical knowledge, strategies

    As mentioned above, the GLOBE project focuses on data collection rather than on data analysis. But where the focus is on data analysis, the project does offer students a good strategy. Students begin by displaying data from their own site. Afterwards they analyze data of a nearby site, so that they will probably find only small differences. Then they analyze the data of sites far away, looking for differences and similarities. Based on these experiences the students generate hypotheses about what factors influence the variable under study and investigate possible effects of these factors on different variables.

    A prototypical sequence is introduced in Data Analysis in the curriculum: seasons investigations.

    Exemplary data analyses, expected answers

    In the Teacher's Guide are notes that outline the kinds of patterns that students might expect to see, including  prototypical graphs, as wellas suggestions for further investigations. However, there are no examples of analyses of moderately complex questions.  Furthermore, some of the prototypical graphs seemed to us ill chosen or not well described. Consider, for example, the graph shown below.


    ( http://globe.fsl.noaa.gov/sda/tg/seasons/img/Fig_3b.GIF)


    The fitted curves were probably drawn freehand, yet this is not explicitly stated.  The materials offer no advice on how a tool might be used that would produce such smooth curves. In fact, freehand curves are problematic.  In this example, the local maximum in February 1997 that appears in the graph is actually non existent.  To see this, we plotted a moving average(28 days) line with Excel (see data analysis in the curriculum: seasons investigations, step 2: average curves).

    In this view, we see in the blue curve with minimum temperatures that during this period there is not an increase and then decrease of the mean, but rather an increase of variation about a constant mean. To be sure, using the 28 days moving average is a somewhat crude method itself, yet we can be fairly certain that the local maximum that appears in the hand-fitted curve is not a reasonable smoothing of the curve. We could check this by means of more advanced smoothing methods.
     

    5. Our Own Exemplary Data Analysis

    For each of these three units Atmosphere Investigation, Hydrology Investigation and Seasons Investigation, we looked for relevant data to answer the questions posed in the project materials. We looked at the Atmosphere Investigation because it is one of the most frequently discussed topics in the classrooms and accounts for approximately 3,500,000 of GLOBE's 4,000,000 measurements.  The Hydrology Investigation is highly related to topics explored in other data-sharing projects such as Wateron the Web or EstuaryNet, allowing comparison of the approaches of the different projects. The Seasons Investigation has a large number of data analyses and many suggestions for the use ofthe projects' online tools.

    Atmosphere Investigation

    Activities in the unit Atmosphere Investigation involve data collection but no genuine data analysis.

    Hydrology Investigation

    In the unit Hydrology Investigation, data collection is in the foreground, but there are some data analysis questions. In one of the unit's chapters  (Water, water everywhere, how does it compare), some time series and notes from scientists on these time series are presented to the students.  The intention is that by studying the graphs presented there, students will acquire a criticalattitude toward data, in particular learning to identify errors or peculiarities in the measurements.

    These 2 investigations follow closely the main orientation of the Globe project, namely focussing on data collection rather than on data analysis. The third unit was more interesting from the perspective of data analysis, and we can summarize it as follows:

    Seasons Investigation

    In the Seasons Investigation, students analyze factors that influence the seasons with the major objective of learning to identify the data and variables needed to investigate particular questions.  The unit is divided into four sections (explanations taken from http://globe.fsl.noaa.gov/sda-bin/wt/ghp/tg+L(en)+P(seasons/LearningActivities ): 
    

Within this unit, we analyzed in depth the questions:  
What are some factors that affect seasonal patterns? and  
How do regional temperature patterns vary among different regions of the world? 

    What are some factors that affect seasonal patterns?
    

    Raised Questions:
    
  This chapter is divided into twelve steps. To start, students use the GLOBE 
  graphing tool to plot a time series of the minimum and maximum temperatures 
  in their school. The students are asked to draw average curves in eachtime series 
  and then compare the average curves (steps 1-3). Afterwards they plot corresponding 
  time series for a school approximately 100 kilometersaway from theirs, and compare 
  this site with theirs using as a guide the following questions: 
    

After completing this task, students construct time series for a school
that is more than 1000 kilometers away and answer the same questions.Finally,

    
the students are asked to develop hypotheses about factors that maybe responsible 
for the differences between the two sites, and then to verify their hypotheses 
with the given data.   They do this, ideally, by "varying" one factor 
systematically while keeping the others constant. 

    Our Analysis:
    
  The comparison of two sites is a focus of the material. The project mainly uses 
  time series plots for this purpose. We made our analysis from the perspective: 
   What are good methods to compare 
  two different sites? from the point 
  of view of statistical data analysis.
    
  
    
  The five questions above are based on comparisons of time series, perhaps because 
  GLOBE provides only line graphs (time series).  However, there are other 
  graphs that would be even more useful in making comparisons between sites, we 
  explored what we can learn from the data when we use scatterplots, 
  histograms, and boxplots. 
  Moreover, we can improve the comparison of two time series if we plot the difference 
  of the two series in one graph.
    

    6. Summary From the Perspective of Data Analysis
    
The data archive of GLOBE is nicely structured.  The project provides several 
useful options for searching and selecting subsets of data andvariables, which 
facilitate finding relevant data for many different tasks and problems. Though 
they have been collected by students, the data are of high quality.  Measurement 
data can be reliably compared because the GLOBE authors have standardized measurement 
procedures and trained students to use the protocols.  Given that the questions 
students pursue could not be answered without data from other schools, data sharing 
is fundamental to the project. Nevertheless, the primary focus of GLOBE from 
the point of view of the students is on data collection rather than data analysis. 

    The project provides a variety of tools, and we found the GLOBE maps particularly 
  powerful and useful.  On the other hand, the GLOBE graphs are fairly rudimentary.  
  If they were to be useful for analyzing data, they would require several additional 
  features.  As it is, in order to analyze data and be able to generate statistical 
  plots such as scatterplots, box plots, and histograms, one needs to use a spread 
  sheet or statistical analysis software tool.
    

    A general criticism is that students are too often asked to make global statements 
  on the basis of single-day measurements. This may help to get an overview of 
  influencing factors and differences; however, this is not a scientifically sound 
  approach.  
    
  
    
  Our impression is that the main focus of the project is to help students appreciate 
  how scientists collect data in a standardized way for scientific purposes.  
  Data analysis is done only in a rudimentary way. At this level, the project 
  seems to achieve its objective quite well.  However, we think the authors 
  could successfully extend the project to place more emphasis on students learning 
  how to analyze data more deeply.